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The Qualitative Deficit

Market research is being deluged with new sources of data, from social media, from electronic communications, and from research communities. Whilst some of this information is suitable for quantitative analysis, large parts of it are unstructured, for example tweets, posts, comments, and uploads. Whilst this data presents an interesting opportunity for market research, it presents a sequence of inter-connected problems and challenges:

Many of the market researchers who are most proficient with unstructured data, the qualitative researchers, are not instinctively drawn towards online data, preferring to deal with people in a face-to-face environment.

Many of the researchers most attracted to large amounts of online data, the hard core quantitative researchers, have little appreciation of the different epistemologies of quantitative and qualitative research.

Many of the software vendors, perhaps in a rush to market, have released products before they were really ready and with massive over-claims.

In order for market research to fully leverage the potential benefits of the discourses being generated, market research needs to address the qualitative deficit. The qualitative deficit is the shortage of talent, software, and approaches designed to utilise massive amounts of qualitative data.

Some of the new approaches and skillsets that are needed relate to the process of quantifying messages within discourses – Google’s Flu Trends being a straightforward example (where Google use phrases that indicate people are searching for flu remedies to quantify the incidence of flu). This use of unstructured data will be of value in areas such as brand and ad tracking.

However, the bigger deficit relates to taking qualitative information and extracting qualitative findings. In online discussions the meaning is often unrelated to the frequency of words and phrases, the meaning rests in the structure of the conversation and the outcomes of the conversation. Feedback for product design, the identification of opportunities, core reasons for product dissatisfaction are likely to be found in the meaning of discourses, as opposed to counts of terms and phrases.

Whilst part of the answer will be new software, my feeling is that research urgently needs to expand the number of researchers with a good understanding of qualitative methods and epistemology.

5 thoughts on “The Qualitative Deficit”

I absolutely agree with you Ray. The potential may be huge, but the skill set among people who work in this area is certainly lacking and the current solutions that offer short cuts (automated analysis solutions) are fun and enticing, but almost certainly misleading.

I agreethat analysts need better understanding of qual methods, but that doesn’t preclude the need for better quantitative analysis of the written world, since that is so immediate and growing so rapidly. As you say, such analysis nees to go much further than simply counting keywords: context is still key to understanding. I have also been looking, unsuccessfully, for some time for a package that could analyse social media output internationally across several languages.

I believe once again that short term “sex appeal” and the desire to be (and be seen as) ahead of the curve will drive purchasing of these solutions in the short term, only to realize that true qualitative insight comes from depth of conversation and interaction, as opposed to data collection method and word counts

Hi Ray, As ever, you’ve hit the nail on the head. We are in the process of developing a crowd-sourced proposition which brings together large teams of qual researchers together to analyse large volumes of unstructured data. This is, of course, only one solution, but goes some way to help give clients the confidence that large-scale qualitative MROCs can deliver value, rather than just adding to the ‘noise.

Hi all: I wanted to share some findings I knew about in Semiofest (a celebration of semiotic thinking), last year in London. There are many people applying semiotics and cultural analysis in order to work with the aspects you are referring to. I found Dario Compagno’s presentation about a tool they developed very interesting. It can process (semantically and semiotically, in order to understand their meaning in context) 50.000 tweets, among other things. Here you have some more information: http://www.socialmediaresearch.biz/english/which-social-media/

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